Periodicity is a frequently happening phenomenon for moving objects. Finding
periodic behaviors is essential to understanding object movements. However,
periodic behaviors could be complicated, involving multiple interleaving periods,
partial time span, and spatiotemporal noises and outliers.
In this paper, we address the problem of mining periodic behaviors for moving
objects. It involves two sub-problems: how to detect the periods in complex
movement, and how to mine periodic movement behaviors. Our main assumption is
that the observed movement is generated from multiple interleaved periodic
behaviors associated with certain reference locations. Based on this assumption,
we propose a two-stage algorithm, Periodica, to solve the problem. At the first
stage, the notion of observation spot is proposed to capture the reference
locations. Through observation spots, multiple periods in the movement can be
retrieved using a method that combines Fourier transform and autocorrelation. At
the second stage, a probabilistic model is proposed to characterize the periodic
behaviors. For a specific period, periodic behaviors are statistically
generalized from partial movement sequences through hierarchical clustering.
Empirical studies on both synthetic and real data sets demonstrate the
effectiveness of our method.